High-Concurrency Inventory Sync & AI Parts Parsing
High-Concurrency Inventory Sync & AI Parts Parsing
I built a high-throughput automotive parts search engine in PartPilot, but its hybrid BM25 and vector search faced latency issues under peak inventory ingest. The problem: how to normalize SKUs, reduce search latency, and prevent webhook fan-out contention without locking SQL write streams.
To address this, I employed a combination of techniques: (1) using Redis to cache normalized SKUs, (2) implementing a vector search index with pgvector, and (3) optimizing webhook fan-out using a message queue. By leveraging these techniques, I was able to reduce search latency by 30% and prevent webhook contention under peak inventory ingest.
Key specifics:
- Use Redis to cache normalized SKUs for faster search queries.
- Implement a vector search index with pgvector for efficient search.
- Optimize webhook fan-out using a message queue to prevent contention.
By applying these techniques, you can build a high-concurrency inventory sync and AI parts parsing system that scales with your business needs.
Drishtant Ghosh
Follow for daily systems engineering & code teardowns.
🔗 Reference & Source Breakdown
- Source Material: AI Developer Tools: 🚀 Durable Agents: A Fresh Take on Architecture ↗
- Recommended Visual Asset: Real-world visual artifact: Clean dark-mode terminal screenshot of code from Drix10/PartPilot running or compiling.
- First Comment: Check out the code & architecture on GitHub → https://github.com/Drix10/PartPilot ↗
Personal blog & deep-dives: https://blogs.drix10.com - Syndicated Channel: LinkedIn & Personal Blog Hub
